Summary

KEY MESSAGE: Alan Agresti and Chris Franklin have merged their research and classroom experience to develop this successful introductory statistics text.Statistics: The Art and Science of Learning from Data, Second Editionhelps readers become statistically literate by encouraging them to ask and answer interesting statistical questions. It takes the ideas that have turned statistics into a central science in modern life and makes them accessible and engaging to readers without compromising necessary rigor. The varied and data-rich examples and exercises place heavy emphasis on thinking about and understanding statistical concepts. The applications are topical and current, and successfully illustrate the relevance of statistics. The authors believe that it is important for readers to be comfortable with analyzing both quantitative and categorical data. Every day in the media, percentages and rates are used to summarize opinion polls, outcomes of medical studies, and economic reports. As a result, greater attention is paid to the analysis of proportions than is typical of many introductory statistics texts. The text maintains its commitment to the recommendations of the ASA endorsed GAISE (Guidelines for Assessment for Instruction in Statistical Education) Report. KEY TOPICS: Statistics: The Art and Science of Learning from Data; Exploring Data with Graphs and Numerical Summaries; Association: Contingency, Correlation, and Regression; Gathering Data; Probability in our Daily Lives; Probability Distributions; Sampling Distributions; Statistical Inference: Confidence Intervals; Statistical Inference: Significance Tests about Hypothesis; Comparing Two Groups; Analyzing the Associations Between Categorical Variables; Analyzing Association Between Quantitative Variables: Regression Analysis; Multiple Regression; Comparing Groups: Analysis of Variance Methods; Nonparametric Statistics MARKET: For all readers interested in statistics.

Table of Contents

Part I: Exploring and Gathering Data 1. Statistics: The Art and Science of Learning from Data 1.1 How Can You Investigate Using Data? 1.2 We Learn about Populations Using Samples 1.3 What Roll do Computers Play in Statistics? 2. Exploring Data with Graphs and Numerical Summaries 2.1What Are the Types of Data? 2.2 How Can We Describe Data Using Graphical Summaries? 2.3 How Can We Describe the Center of Quantitative Data? 2.4 How Can We Describe the Spread of Quantitative Data? 2.5 How Can Measures of Position Describe Spread? 2.6 How are Graphical Displays Misused? 3. Association: Contingency, Correlation, and Regression 3.1 How Can We Explore the Association between Two Categorical Variables? 3.2 How Can We Explore the Association between Two Quantitative Variables? 3.3 How Can We Predict the Outcome of a Variable? 3.4 What are Some Cautions in Analyzing Associations? 4. Gathering Data 4.1 Should We Experiment or Should We Merely Observe? 4.2 What Are Good Ways and Poor Ways to Sample? 4.3 What Are Good Ways and Poor Ways to Experiment? 4.4 What Are Other Ways to Perform Experimental and Non-experimental Studies? PART I REVIEW

Part II: Probability, Probability Distributions, and Sampling Distributions 5. Probability in our Daily Lives 5.1 How Can Probability Quantify Randomness? 5.2 How Can We Find Probabilities? 5.3 Conditional Probability: Whatrsquo;s the Probability of A, Given B? 5.4 Applying the Probability Rules 6. Probability Distributions 6.1 How Can We Summarize the Distributions of Discrete and Continuous Random Variables? 6.2 How Can We Find Probabilities for the Normal Distribution? 6.3 How Can We Find Probabilities for the Binomial? 7. Sampling Distributions 7.1 How Likely Are the Possible Values of a Statistic?: The Sampling Dis

KEY MESSAGE: Alan Agresti and Chris Franklin have merged their research and classroom experience to develop this successful introductory statistics text.Statistics: The Art and Science of Learning from Data, Second Editionhelps readers become statistically literate by encouraging them to ask and answer interesting statistical questions. It takes the ideas that have turned statistics into a central science in modern life and makes them accessible and engaging to readers without compromising necessary rigor. The varied and data-rich examples and exercises place heavy emphasis on thinking about and understanding statistical concepts. The applications are topical and current, and successfully illustrate the relevance of statistics. The authors believe that it is important for readers to be comfortable with analyzing both quantitative and categorical data. Every day in the media, percentages and rates are used to summarize opinion polls, outcomes of medical studies, and economic reports. As a result, greater attention is paid to the analysis of proportions than is typical of many introductory statistics texts. The text maintains its commitment to the recommendations of the ASA endorsed GAISE (Guidelines for Assessment for Instruction in Statistical Education) Report. KEY TOPICS: Statistics: The Art and Science of Learning from Data; Exploring Data with Graphs and Numerical Summaries; Association: Contingency, Correlation, and Regression; Gathering Data; Probability in our Daily Lives; Probability Distributions; Sampling Distributions; Statistical Inference: Confidence Intervals; Statistical Inference: Significance Tests about Hypothesis; Comparing Two Groups; Analyzing the Associations Between Categorical Variables; Analyzing Association Between Quantitative Variables: Regression Analysis; Multiple Regression; Comparing Groups: Analysis of Variance Methods; Nonparametric Statistics MARKET: For all readers interested in statistics.

Part I: Exploring and Gathering Data 1. Statistics: The Art and Science of Learning from Data 1.1 How Can You Investigate Using Data? 1.2 We Learn about Populations Using Samples 1.3 What Roll do Computers Play in Statistics? 2. Exploring Data with Graphs and Numerical Summaries 2.1What Are the Types of Data? 2.2 How Can We Describe Data Using Graphical Summaries? 2.3 How Can We Describe the Center of Quantitative Data? 2.4 How Can We Describe the Spread of Quantitative Data? 2.5 How Can Measures of Position Describe Spread? 2.6 How are Graphical Displays Misused? 3. Association: Contingency, Correlation, and Regression 3.1 How Can We Explore the Association between Two Categorical Variables? 3.2 How Can We Explore the Association between Two Quantitative Variables? 3.3 How Can We Predict the Outcome of a Variable? 3.4 What are Some Cautions in Analyzing Associations? 4. Gathering Data 4.1 Should We Experiment or Should We Merely Observe? 4.2 What Are Good Ways and Poor Ways to Sample? 4.3 What Are Good Ways and Poor Ways to Experiment? 4.4 What Are Other Ways to Perform Experimental and Non-experimental Studies? PART I REVIEW

Part II: Probability, Probability Distributions, and Sampling Distributions 5. Probability in our Daily Lives 5.1 How Can Probability Quantify Randomness? 5.2 How Can We Find Probabilities? 5.3 Conditional Probability: Whatrsquo;s the Probability of A, Given B? 5.4 Applying the Probability Rules 6. Probability Distributions 6.1 How Can We Summarize the Distributions of Discrete and Continuous Random Variables? 6.2 How Can We Find Probabilities for the Normal Distribution? 6.3 How Can We Find Probabilities for the Binomial? 7. Sampling Distributions 7.1 How Likely Are the Possible Values of a Statistic?: The Sampling Dis

Summary

KEY MESSAGE: Alan Agresti and Chris Franklin have merged their research and classroom experience to develop this successful introductory statistics text.Statistics: The Art and Science of Learning from Data, Second Editionhelps readers become statistically literate by encouraging them to ask and answer interesting statistical questions. It takes the ideas that have turned statistics into a central science in modern life and makes them accessible and engaging to readers without compromising necessary rigor. The varied and data-rich examples and exercises place heavy emphasis on thinking about and understanding statistical concepts. The applications are topical and current, and successfully illustrate the relevance of statistics. The authors believe that it is important for readers to be comfortable with analyzing both quantitative and categorical data. Every day in the media, percentages and rates are used to summarize opinion polls, outcomes of medical studies, and economic reports. As a result, greater attention is paid to the analysis of proportions than is typical of many introductory statistics texts. The text maintains its commitment to the recommendations of the ASA endorsed GAISE (Guidelines for Assessment for Instruction in Statistical Education) Report. KEY TOPICS: Statistics: The Art and Science of Learning from Data; Exploring Data with Graphs and Numerical Summaries; Association: Contingency, Correlation, and Regression; Gathering Data; Probability in our Daily Lives; Probability Distributions; Sampling Distributions; Statistical Inference: Confidence Intervals; Statistical Inference: Significance Tests about Hypothesis; Comparing Two Groups; Analyzing the Associations Between Categorical Variables; Analyzing Association Between Quantitative Variables: Regression Analysis; Multiple Regression; Comparing Groups: Analysis of Variance Methods; Nonparametric Statistics MARKET: For all readers interested in statistics.

Table of Contents

Table of Contents

Part I: Exploring and Gathering Data 1. Statistics: The Art and Science of Learning from Data 1.1 How Can You Investigate Using Data? 1.2 We Learn about Populations Using Samples 1.3 What Roll do Computers Play in Statistics? 2. Exploring Data with Graphs and Numerical Summaries 2.1What Are the Types of Data? 2.2 How Can We Describe Data Using Graphical Summaries? 2.3 How Can We Describe the Center of Quantitative Data? 2.4 How Can We Describe the Spread of Quantitative Data? 2.5 How Can Measures of Position Describe Spread? 2.6 How are Graphical Displays Misused? 3. Association: Contingency, Correlation, and Regression 3.1 How Can We Explore the Association between Two Categorical Variables? 3.2 How Can We Explore the Association between Two Quantitative Variables? 3.3 How Can We Predict the Outcome of a Variable? 3.4 What are Some Cautions in Analyzing Associations? 4. Gathering Data 4.1 Should We Experiment or Should We Merely Observe? 4.2 What Are Good Ways and Poor Ways to Sample? 4.3 What Are Good Ways and Poor Ways to Experiment? 4.4 What Are Other Ways to Perform Experimental and Non-experimental Studies? PART I REVIEW

Part II: Probability, Probability Distributions, and Sampling Distributions 5. Probability in our Daily Lives 5.1 How Can Probability Quantify Randomness? 5.2 How Can We Find Probabilities? 5.3 Conditional Probability: Whatrsquo;s the Probability of A, Given B? 5.4 Applying the Probability Rules 6. Probability Distributions 6.1 How Can We Summarize the Distributions of Discrete and Continuous Random Variables? 6.2 How Can We Find Probabilities for the Normal Distribution? 6.3 How Can We Find Probabilities for the Binomial? 7. Sampling Distributions 7.1 How Likely Are the Possible Values of a Statistic?: The Sampling Dis